Abstract 3143: A bioinformatic pipeline for identifying change-of-metabolic-function cancer mutations

Abstract Cancer arises through clonal evolution, in some cases selecting change of metabolic function (COMF) mutations that functionally alter proteins to affect cellular metabolism. COMF mutations can impart new catalytic functions that can be applied to enable novel chemical synthesis and metabolic engineering techniques. We previously showed that the oncogenic COMF mutation IDH1 p.R132H could also be used to enable a bio-based method of adipic acid production, a valuable commodity chemical used to synthesize nylon that erstwhile required petroleum-substrates for synthesis. However, there is no high-throughput method to identify COMF mutations. Here, we present METIS (Mutated Enzymes from Tumors In silico Screen), a bioinformatic pipeline to identify COMF mutations from cancer mutational data based on the recurrence rate, genetic conservation, and predicted functionality of the mutations. We applied METIS to 210,354 cancer-derived missense mutations from the COSMIC (Catalogue Of Somatic Mutations In Cancer) database. We identified 4 candidate COMF mutations: the Cbl proto-oncogene E3 ubiquitin ligase (CBL) p.Y371H, Polypeptide N-Acetylgalactosaminyltransferase 17 (GALNT17) p.R228C, solute carrier family 17 member 5 (SLC17A5) p.R364C, and 2-oxoglutarate dehydrogenase-like (OGDHL) p.A400T. To determine these mutations’ effect on the cellular metabolome, we performed unbiased global metabolite profiling using LC-MS/MS and GC/MS of HeLa cells exogenously expressing COMF candidates. OGDHL p.A400T demonstrated significant metabolic changes after FDR correction (q<0.05, two-tailed Welch’s unequal variances t-test with Bonferroni correction). In particular, xanthosine, a key intermediate in purine metabolism commonly used in pharmaceutical development, was increased 2.9-fold (P = 1.2 x 10-9, q = 3.2 x 10-7). Thus, our data suggest OGDHL p.A400T could be used to improve production methods for a useful biochemical. We then deployed METIS2, which featured improved statistical, pathogenicity, and structural analysis tools to all 49 million currently-available cancer mutations within COSMIC, providing a refined panel of six candidate mutations. Consistent with the findings from initial METIS metabolomic screen, METIS2 identified OGDHLp.A400T as a COMF mutation candidate. Overall, our results detail an approach to filter cancer data for mutations that confer metabolic functions, validate that mutations identified in this way can alter the cellular metabolome, and catalog potentially-useful candidate mutations. Moreover, prediction of COMF mutations through METIS can also be applied to elucidate mechanisms of cancer initiation, progression, and/or maintenance to identify potential therapeutic targets. As cancer mutation and structural data continue to accumulate, we expect METIS to increase in its predictive power to accurately find COMF mutations. Citation Format: Kevin J. Tu, Bill H. Diplas, Joshua A. Regal, Matthew S. Waitkus, Christopher J. Pirozzi, Zachary J. Reitman. A bioinformatic pipeline for identifying change-of-metabolic-function cancer mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3143.

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